Balanced maintenance method and system for full-automatic running vehicle base

Abstract

The invention discloses a balanced maintenance method and system for a full-automatic running vehicle base, and the method comprises the steps: obtaining train maintenance information which comprises a maintenance type and a total train number; the time of each maintenance type, the remaining running time of each train to next maintenance, the number of maintenance personnel required for maintenance of each maintenance type, and the average maintenance time of each maintenance type; and setting a minimum occupied resource loss function according to the train maintenance information, calculating a minimum occupied resource index of a maintenance train according to the train maintenance information, and allocating required resources according to the minimum occupied resource index.

Description

Technical Field

[0001] The present invention belongs to the technical field of train maintenance, and more specifically, relates to a balanced maintenance method and system for a fully automatic vehicle base. Background Art

[0002] With the rapid development of urban rail transit in China, the public transportation share rate of large and medium-sized cities is getting higher and higher, which greatly improves the level of urban traffic management. While urban rail transit is developing rapidly, the vehicle base, as an essential facility for rail transit, occupies a large amount of urban construction land, which restricts the development of the city to a certain extent; secondly, with the advancement of automation technology, fully automatic operation lines have been opened in cities such as Beijing, Shanghai, and Shenzhen, and the construction scale of fully automatic operation vehicle bases has not been effectively optimized; thirdly, the procurement cost of urban rail transit trains accounts for 10% to 20% of the entire project construction investment, and it is particularly important to improve the turnover efficiency of trains as much as possible based on the maintenance system. Therefore, the fully automatic operation vehicle base system and method based on the balanced maintenance mode is of great significance to the sustainable development of urban rail transit.

[0003] At present, the design and construction of domestic vehicle bases generally refer to the "Metro Design Code" (GB50157), lacking special systems and methods for fully automatic operation and balanced maintenance modes. There are problems such as poor matching of operation and maintenance processes, and large amounts of idle land and trains. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a balanced maintenance method for a fully automatic vehicle base, comprising:

[0005] Obtain train maintenance information, wherein the train maintenance information includes: maintenance type, total number of trains, time for each maintenance type, remaining running time of each train until the next maintenance, number of maintenance personnel required for each maintenance type, and average maintenance time for each maintenance type;

[0006] A function for minimizing the loss of occupied resources is set, and based on the train maintenance information, a minimum occupied resource index of the maintenance train is calculated, and based on the minimum occupied resource index, required resources are allocated.

[0007] Furthermore, the function of minimizing the occupied resource loss includes:

[0008]

[0009] Where M is the number of maintenance types, N is the total number of trains, and x ij is the time allocated to the jth train for the ith maintenance type, E i is the efficiency coefficient of the ith maintenance type, Li is the priority coefficient of the i-th maintenance type, α is the first weight factor, R j is the remaining running time of the jth train until the next maintenance, ∈ is a normal number, β is the second weight factor, γ is the third weight factor, δ is the fourth weight factor, C i is the number of maintenance personnel required for the i-th maintenance type, T i is the average maintenance time of the i-th maintenance type.

[0010] Furthermore, the efficiency coefficient E of the i-th maintenance type is i include:

[0011]

[0012] Among them, T min is the minimum average maintenance time among all maintenance types, σ i is the standard deviation of the maintenance time for the ith maintenance type, σ max is the maximum standard deviation of maintenance time among all maintenance types, C avg is the average number of maintenance personnel required for all types of maintenance, P i is the cost required for the i-th maintenance type, P max is the maximum cost required for all maintenance types, α′ is the first weighting factor of the efficiency coefficient, β′ is the second weighting factor of the efficiency coefficient, γ′ is the third weighting factor of the efficiency coefficient, and δ′ is the fourth weighting factor of the efficiency coefficient.

[0013] Furthermore, the priority coefficient L of the i-th maintenance type is i include:

[0014]

[0015] Among them, F i is the occurrence frequency of the ith maintenance type, F max is the maximum occurrence frequency among all maintenance types, η′ is the first weight of the priority coefficient, I i is the score of the impact of the i-th maintenance type on train failure, I max is the score for the greatest impact among all maintenance types, θ′ is the second weight of the priority coefficient, U i Score the importance of the i-th maintenance type, U max is the maximum priority score among all maintenance types, and κ′ is the third weight of the priority coefficient.

[0016] Furthermore, it also includes setting constraints:

[0017]

[0018] Wherein, β″ is the first adjustment factor, and γ′ is the second adjustment factor.

[0019] The present invention also provides a balanced maintenance system for a fully automatic vehicle base, comprising:

[0020] The information acquisition module is used to acquire train maintenance information, wherein the train maintenance information includes: maintenance type, total number of trains, time for each maintenance type, remaining running time of each train until the next maintenance, number of maintenance personnel required for each maintenance type, and average maintenance time for each maintenance type;

[0021] The resource allocation module is used to set a function for minimizing the loss of occupied resources, and calculate the minimum occupied resource index of the maintenance train according to the train maintenance information, and allocate the required resources according to the minimum occupied resource index.

[0022] Furthermore, the function of minimizing the occupied resource loss includes:

[0023]

[0024] Where M is the number of maintenance types, N is the total number of trains, and x ij is the time allocated to the jth train for the ith maintenance type, E i is the efficiency coefficient of the ith maintenance type, L i is the priority coefficient of the i-th maintenance type, α is the first weight factor, R j is the remaining running time of the jth train until the next maintenance, ∈ is a normal number, β is the second weight factor, γ is the third weight factor, δ is the fourth weight factor, C i is the number of maintenance personnel required for the i-th maintenance type, T i is the average maintenance time of the i-th maintenance type.

[0025] Furthermore, the efficiency coefficient E of the i-th maintenance type is i include:

[0026]

[0027] Among them, T min is the minimum average maintenance time among all maintenance types, σ i is the standard deviation of the maintenance time for the ith maintenance type, σ max is the maximum standard deviation of maintenance time among all maintenance types, C avg is the average number of maintenance personnel required for all types of maintenance, P i is the cost required for the i-th maintenance type, P maxis the maximum cost required for all maintenance types, α′ is the first weighting factor of the efficiency coefficient, β′ is the second weighting factor of the efficiency coefficient, γ′ is the third weighting factor of the efficiency coefficient, and δ′ is the fourth weighting factor of the efficiency coefficient.

[0028] Furthermore, the priority coefficient L of the i-th maintenance type is i include:

[0029]

[0030] Among them, F i is the occurrence frequency of the ith maintenance type, F max is the maximum occurrence frequency among all maintenance types, η′ is the first weight of the priority coefficient, I i is the score of the impact of the i-th maintenance type on train failure, I max is the score for the greatest impact among all maintenance types, θ′ is the second weight of the priority coefficient, U i Score the importance of the i-th maintenance type, U max is the maximum priority score among all maintenance types, and κ′ is the third weight of the priority coefficient.

[0031] Furthermore, it also includes setting constraints:

[0032]

[0033] Wherein, β″ is the first adjustment factor, and γ′ is the second adjustment factor.

[0034] Compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:

[0035] 1. The present invention refines the maintenance process, increases the maintenance frequency of trains, and ensures that all trains can receive timely and efficient maintenance services.

[0036] 2. The present invention shortens the maintenance time of each train, improves the turnover efficiency and online rate of the train, reduces the configuration of maintenance trains, avoids the waste of maintenance resources, and reduces engineering construction investment.

[0037] 3. The present invention optimizes the warehouse combination by matching the maintenance schedule, thereby reducing the floor area of ​​the warehouse, achieving intensive use of land, and providing support for the sustainable development of urban rail transit. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is an overall flow chart of Embodiment 1 of the present invention;

[0039] Figure 2 is a flow chart of the method of embodiment 1 of the present invention;

[0040] Figure 3 It is a system structure diagram of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0041] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0042] The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

[0043] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts in the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.

[0044] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets or instructions.

[0045] The display is used to show the interactive cross-section of each application.

[0046] All subscripts in the formulas of the present invention are only used to distinguish parameters and have no actual meaning.

[0047] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

[0048] Example 1

[0049] like Figure 1 As shown in the figure, at the data analysis level: by analyzing the technical characteristics of the trains and the conventional planned maintenance patterns (annual inspection, three-month inspection, and biweekly inspection), the train maintenance components, train downtime, and maintenance facility configuration are summarized and summarized, and the maintenance content is broken down into several small maintenance procedures to increase the maintenance frequency and shorten the maintenance downtime.

[0050] Strategy formation level: According to the actual needs of the train and the availability of maintenance resources, balanced scheduling is carried out, and several small maintenance procedures are classified and summarized to form special maintenance, balanced maintenance and special maintenance; and balanced maintenance operation instructions for each system of the train and maintenance operation process cards for each maintenance procedure are formulated in turn, among which, 1. Special maintenance: including coupler coupling function inspection; pantograph torque verification; air-conditioning cabinet 380V terminal temperature measurement; desiccant census and periodic replacement; vehicle traction brake control system dynamic adjustment test and other operations.

[0051] 2. Balanced maintenance: including cleaning of the car body and passenger compartment; checking whether the car doors have power or not; checking the driver's cab; checking the interior condition of the gangway passenger compartment; operating the brake function; checking the ventilation and cooling functions of the passenger compartment; checking other main circuit components; operating the driver's controller; operating the traction brake control system with power or without power; operating the train communication control system; operating the PIS system; operating the lighting system, etc.

[0052] 3. Special repairs: including inspection of the boarding ladder; inspection of the gangway; operation of the coupler system; operation of the bogie; operation of the air supply unit with or without power; operation of the brake control system and its auxiliary equipment; operation of the tread brake unit; other operations of the brake function and the brake system; operation of the air conditioning system; operation of the main circuit system; other main circuit components; operation of the auxiliary system, etc.

[0053] Analyze and summarize the maintenance process according to different maintenance contents to reduce train downtime.

[0054] Strategy application level: Through the upgrade of maintenance system and maintenance mode, targeted optimization and improvement are carried out from three aspects: train configuration, warehouse combination and maintenance facilities. In terms of train configuration, by refining the maintenance process and shortening the train depot stop time, the purpose of reducing train depot stop is finally achieved, the train turnover efficiency and online rate are improved, and the number of train maintenance and purchase costs are reduced; in terms of warehouse combination, the number of balanced maintenance positions is determined according to calculation, and the tracks without maintenance needs are combined into parking garages, and the tracks with maintenance needs are combined into maintenance warehouses. The maintenance warehouses can be further subdivided into balanced maintenance warehouses, special maintenance warehouses and special maintenance warehouses, and the track spacing is refined and the warehouse scale is compressed; in terms of maintenance facilities, according to the maintenance needs of different maintenance processes, targeted settings are made according to no maintenance platform, medium maintenance platform and medium and high maintenance platform.

[0055] like Figure 2 As shown, an embodiment of the present invention provides a balanced maintenance method for a fully automatic vehicle base, comprising:

[0056] Step 101, obtaining train maintenance information, wherein the train maintenance information includes: maintenance type, total number of trains, time for each maintenance type, remaining running time of each train until the next maintenance, number of maintenance personnel required for each maintenance type, average maintenance time for each maintenance type;

[0057] Step 102, setting a function for minimizing the loss of occupied resources, and calculating a minimum occupied resource index of the maintenance train according to the train maintenance information, and allocating required resources according to the minimum occupied resource index.

[0058] Specifically, the function of minimizing the occupied resource loss includes:

[0059]

[0060] Where M is the number of maintenance types, N is the total number of trains, and x ij is the time allocated to the jth train for the ith maintenance type, E i is the efficiency coefficient of the ith maintenance type, L i is the priority coefficient of the i-th maintenance type, α is the first weight factor, R j is the remaining running time of the jth train until the next maintenance, ∈ is a normal number, β is the second weight factor, γ is the third weight factor, δ is the fourth weight factor, C i is the number of maintenance personnel required for the i-th maintenance type, T i is the average maintenance time of the i-th maintenance type.

[0061] Specifically, the efficiency coefficient E of the i-th maintenance type is i include:

[0062]

[0063] Among them, T min is the minimum average maintenance time among all maintenance types, σ i is the standard deviation of the maintenance time for the ith maintenance type, σ max is the maximum standard deviation of maintenance time among all maintenance types, C avg is the average number of maintenance personnel required for all types of maintenance, P i is the cost required for the i-th maintenance type, P max is the maximum cost required for all maintenance types, α′ is the first weighting factor of the efficiency coefficient, β′ is the second weighting factor of the efficiency coefficient, γ′ is the third weighting factor of the efficiency coefficient, and δ′ is the fourth weighting factor of the efficiency coefficient.

[0064] Specifically, the priority coefficient L of the i-th maintenance type isi include:

[0065]

[0066] Among them, F i is the occurrence frequency of the ith maintenance type, F max is the maximum occurrence frequency among all maintenance types, η′ is the first weight of the priority coefficient, I i is the score of the impact of the i-th maintenance type on train failure, I max is the score for the greatest impact among all maintenance types, θ′ is the second weight of the priority coefficient, U i Score the importance of the i-th maintenance type, U max is the maximum priority score among all maintenance types, and κ′ is the third weight of the priority coefficient.

[0067] For example, the impact of the i-th maintenance type on train failure is scored as I i :

[0068] 1-2 points: Almost no impact on train failure.

[0069] 3-4 points: The failure has a slight impact on the train, but will not cause the train to stop operating.

[0070] 5-6 points: There is a moderate impact on the train failure, which may cause train delays.

[0071] 7-8 points: There is a significant impact on the train failure, which may cause the train to stop or be seriously delayed.

[0072] 9-10 points: It has a great impact on train failure, which will inevitably lead to train suspension and may affect the entire operating network.

[0073] For example, the importance score U of the i-th maintenance type is i :

[0074] 1-2 points: Not a very important maintenance, which can be performed occasionally.

[0075] 3-4 points: Relatively important maintenance, which can improve system stability if performed regularly.

[0076] 5-6 points: Moderately important maintenance that needs to be performed regularly to ensure the normal operation of the system.

[0077] 7-8 points: Very important maintenance that must be performed regularly to avoid potential failures.

[0078] 9-10 points: Extremely important maintenance that must be performed frequently to ensure system safety and stability.

[0079] Specifically, it also includes setting constraints:

[0080]

[0081] Wherein, β″ is the first adjustment factor, and γ′ is the second adjustment factor.

[0082] Example 2

[0083] like Figure 2 As shown, the embodiment of the present invention also provides a balanced maintenance system for a fully automatic vehicle base, including:

[0084] The information acquisition module is used to acquire train maintenance information, wherein the train maintenance information includes: maintenance type, total number of trains, time for each maintenance type, remaining running time of each train until the next maintenance, number of maintenance personnel required for each maintenance type, and average maintenance time for each maintenance type;

[0085] The resource allocation module is used to set a function for minimizing the loss of occupied resources, and calculate the minimum occupied resource index of the maintenance train according to the train maintenance information, and allocate the required resources according to the minimum occupied resource index.

[0086] Specifically, the function of minimizing the occupied resource loss includes:

[0087]

[0088] Where M is the number of maintenance types, N is the total number of trains, and x ij is the time allocated to the jth train for the ith maintenance type, E i is the efficiency coefficient of the ith maintenance type, L i is the priority coefficient of the i-th maintenance type, α is the first weight factor, R j is the remaining running time of the jth train until the next maintenance, ∈ is a normal number, β is the second weight factor, γ is the third weight factor, δ is the fourth weight factor, C i is the number of maintenance personnel required for the i-th maintenance type, T i is the average maintenance time of the i-th maintenance type.

[0089] Specifically, the efficiency coefficient E of the i-th maintenance type is i include:

[0090]

[0091] Among them, T min is the minimum average maintenance time among all maintenance types, σ i is the standard deviation of the maintenance time for the ith maintenance type, σmax is the maximum standard deviation of maintenance time among all maintenance types, C avg is the average number of maintenance personnel required for all types of maintenance, P i is the cost required for the i-th maintenance type, P max is the maximum cost required for all maintenance types, α′ is the first weighting factor of the efficiency coefficient, β′ is the second weighting factor of the efficiency coefficient, γ′ is the third weighting factor of the efficiency coefficient, and δ′ is the fourth weighting factor of the efficiency coefficient.

[0092] Specifically, the priority coefficient L of the i-th maintenance type is i include:

[0093]

[0094] Among them, F i is the occurrence frequency of the ith maintenance type, F max is the maximum occurrence frequency among all maintenance types, η′ is the first weight of the priority coefficient, I i is the score of the impact of the i-th maintenance type on train failure, I max is the score for the greatest impact among all maintenance types, θ′ is the second weight of the priority coefficient, U i Score the importance of the i-th maintenance type, U max is the maximum priority score among all maintenance types, and κ′ is the third weight of the priority coefficient.

[0095] Specifically, it also includes setting constraints:

[0096]

[0097] Wherein, β″ is the first adjustment factor, and γ′ is the second adjustment factor.

[0098] Example 3

[0099] The embodiment of the present invention further provides a storage medium storing a plurality of instructions, wherein the instructions are used to implement the balanced maintenance method of a fully automatic vehicle base.

[0100] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0101] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: Step 101, obtaining train maintenance information, wherein the train maintenance information includes: maintenance type, total number of trains, time for each maintenance type, remaining running time of each train until the next maintenance, number of maintenance personnel required for each maintenance type, average maintenance time for each maintenance type;

[0102] Step 102, setting a function for minimizing the loss of occupied resources, and calculating a minimum occupied resource index of the maintenance train according to the train maintenance information, and allocating required resources according to the minimum occupied resource index.

[0103] Specifically, the function of minimizing the occupied resource loss includes:

[0104]

[0105] Where M is the number of maintenance types, N is the total number of trains, and x ij is the time allocated to the jth train for the ith maintenance type, E i is the efficiency coefficient of the ith maintenance type, L i is the priority coefficient of the i-th maintenance type, α is the first weight factor, R j is the remaining running time of the jth train until the next maintenance, ∈ is a normal number, β is the second weight factor, γ is the third weight factor, δ is the fourth weight factor, C i is the number of maintenance personnel required for the i-th maintenance type, T i is the average maintenance time of the i-th maintenance type.

[0106] Specifically, the efficiency coefficient E of the i-th maintenance type is i include:

[0107]

[0108] Among them, T min is the minimum average maintenance time among all maintenance types, σ i is the standard deviation of the maintenance time for the ith maintenance type, σ max is the maximum standard deviation of maintenance time among all maintenance types, C avg is the average number of maintenance personnel required for all types of maintenance, P i is the cost required for the i-th maintenance type, P max is the maximum cost required for all maintenance types, α′ is the first weighting factor of the efficiency coefficient, β′ is the second weighting factor of the efficiency coefficient, γ′ is the third weighting factor of the efficiency coefficient, and δ′ is the fourth weighting factor of the efficiency coefficient.

[0109] Specifically, the priority coefficient L of the i-th maintenance type is i include:

[0110]

[0111] Among them, F i is the occurrence frequency of the ith maintenance type, F max is the maximum occurrence frequency among all maintenance types, η′ is the first weight of the priority coefficient, I i is the score of the impact of the i-th maintenance type on train failure, I max is the score for the greatest impact among all maintenance types, θ′ is the second weight of the priority coefficient, U i Score the importance of the i-th maintenance type, U max is the maximum priority score among all maintenance types, and κ′ is the third weight of the priority coefficient.

[0112] Specifically, it also includes setting constraints:

[0113]

[0114] Wherein, β″ is the first adjustment factor, and γ′ is the second adjustment factor.

[0115] Example 4

[0116] An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores multiple instructions, and the instructions can be loaded and executed by the processor to enable the processor to execute a balancing maintenance method for a fully automatic vehicle base.

[0117] Specifically, the electronic device of this embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.

[0118] Among them, the storage medium can be used to store software programs and modules, such as a balanced maintenance method for a fully automatic vehicle base in an embodiment of the present invention, and corresponding program instructions / modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned balanced maintenance method for a fully automatic vehicle base. The storage medium may include high-speed random storage media, and may also include non-volatile storage media, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include storage media remotely arranged relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0119] The processor can call the information and application program stored in the storage medium through the transmission system to execute the steps: Step 101, obtaining train maintenance information, wherein the train maintenance information includes: maintenance type, total number of trains, time of each maintenance type, remaining running time of each train until the next maintenance, number of maintenance personnel required for each maintenance type, average maintenance time of each maintenance type;

[0120] Step 102, setting a function for minimizing the loss of occupied resources, and calculating a minimum occupied resource index of the maintenance train according to the train maintenance information, and allocating required resources according to the minimum occupied resource index.

[0121] Specifically, the function of minimizing the occupied resource loss includes:

[0122]

[0123] Where M is the number of maintenance types, N is the total number of trains, and x ij is the time allocated to the jth train for the ith maintenance type, E i is the efficiency coefficient of the ith maintenance type, L i is the priority coefficient of the i-th maintenance type, α is the first weight factor, R j is the remaining running time of the jth train until the next maintenance, ∈ is a normal number, β is the second weight factor, γ is the third weight factor, δ is the fourth weight factor, C i is the number of maintenance personnel required for the i-th maintenance type, T i is the average maintenance time of the i-th maintenance type.

[0124] Specifically, the efficiency coefficient E of the i-th maintenance type is i include:

[0125]

[0126] Among them, T min is the minimum average maintenance time among all maintenance types, σ i is the standard deviation of the maintenance time for the ith maintenance type, σ max is the maximum standard deviation of maintenance time among all maintenance types, C avg is the average number of maintenance personnel required for all types of maintenance, P i is the cost required for the i-th maintenance type, P max is the maximum cost required for all maintenance types, α′ is the first weighting factor of the efficiency coefficient, β′ is the second weighting factor of the efficiency coefficient, γ′ is the third weighting factor of the efficiency coefficient, and δ′ is the fourth weighting factor of the efficiency coefficient.

[0127] Specifically, the priority coefficient L of the i-th maintenance type is i include:

[0128]

[0129] Among them, F i is the occurrence frequency of the ith maintenance type, F max is the maximum occurrence frequency among all maintenance types, η′ is the first weight of the priority coefficient, I i is the score of the impact of the i-th maintenance type on train failure, I max is the score for the greatest impact among all maintenance types, θ′ is the second weight of the priority coefficient, U i Score the importance of the i-th maintenance type, U max is the maximum priority score among all maintenance types, and κ′ is the third weight of the priority coefficient.

[0130] Specifically, it also includes setting constraints:

[0131]

[0132] Wherein, β″ is the first adjustment factor, and γ′ is the second adjustment factor.

[0133] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0134] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0135] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0136] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0137] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0138] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0139] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A balanced maintenance method for a fully automatic vehicle base, characterized in that: include: Obtaining train maintenance information, wherein the train maintenance information includes: maintenance type, total number of trains; time for each maintenance type, remaining running time of each train until the next maintenance, number of maintenance personnel required for each maintenance type, and average maintenance time for each maintenance type; A function for minimizing the loss of occupied resources is set, and based on the train maintenance information, a minimum occupied resource index of the maintenance train is calculated, and based on the minimum occupied resource index, required resources are allocated.

2. A balanced maintenance method for a fully automatic vehicle base as claimed in claim 1, characterized in that: The function of minimizing occupied resource loss includes: Where M is the number of maintenance types, N is the total number of trains, and x ij is the time allocated to the jth train for the ith maintenance type, E i is the efficiency coefficient of the ith maintenance type, L i is the priority coefficient of the i-th maintenance type, α is the first weight factor, R j is the remaining running time of the jth train until the next maintenance, ∈ is a normal number, β is the second weight factor, γ is the third weight factor, δ is the fourth weight factor, C i is the number of maintenance personnel required for the i-th maintenance type, T i is the average maintenance time of the i-th maintenance type.

3. A balanced maintenance method for a fully automatic vehicle base as claimed in claim 2, characterized in that: The efficiency coefficient E of the i-th maintenance type i include: Among them, T min is the minimum average maintenance time among all maintenance types, σ i is the standard deviation of the maintenance time for the ith maintenance type, σ max is the maximum standard deviation of maintenance time among all maintenance types, C avg is the average number of maintenance personnel required for all types of maintenance, P i is the cost required for the i-th maintenance type, P max is the maximum cost required for all maintenance types, α′ is the first weighting factor of the efficiency coefficient, β′ is the second weighting factor of the efficiency coefficient, γ′ is the third weighting factor of the efficiency coefficient, and δ′ is the fourth weighting factor of the efficiency coefficient.

4. A balanced maintenance method for a fully automatic vehicle base as claimed in claim 2, characterized in that: The priority coefficient L of the i-th maintenance type i include: Among them, F i is the occurrence frequency of the ith maintenance type, F max is the maximum occurrence frequency among all maintenance types, η′ is the first weight of the priority coefficient, I i is the score of the impact of the i-th maintenance type on train failure, I max is the score for the greatest impact among all maintenance types, θ′ is the second weight of the priority coefficient, U i Score the importance of the i-th maintenance type, U max is the maximum priority score among all maintenance types, and κ′ is the third weight of the priority coefficient.

5. The method for balancing and repairing a fully automatic vehicle base as claimed in claim 2, characterized in that: It also includes setting constraints: Wherein, β″ is the first adjustment factor, and γ′ is the second adjustment factor.

6. A balanced maintenance system for a fully automatic vehicle base, characterized in that: include: An information acquisition module is used to acquire train maintenance information, wherein the train maintenance information includes: maintenance type, total number of trains; time for each maintenance type, remaining running time of each train until the next maintenance, number of maintenance personnel required for each maintenance type, and average maintenance time for each maintenance type; The resource allocation module is used to set a function for minimizing the loss of occupied resources, and calculate the minimum occupied resource index of the maintenance train according to the train maintenance information, and allocate the required resources according to the minimum occupied resource index.

7. A balanced maintenance system for a fully automatic vehicle base as claimed in claim 6, characterized in that: The function of minimizing occupied resource loss includes: Where M is the number of maintenance types, N is the total number of trains, and x ij is the time allocated to the jth train for the ith maintenance type, E i is the efficiency coefficient of the ith maintenance type, L i is the priority coefficient of the i-th maintenance type, α is the first weight factor, R j is the remaining running time of the jth train until the next maintenance, ∈ is a normal number, β is the second weight factor, γ is the third weight factor, δ is the fourth weight factor, C i is the number of maintenance personnel required for the i-th maintenance type, T i is the average maintenance time of the i-th maintenance type.

8. A balanced maintenance system for a fully automatic vehicle base as claimed in claim 7, characterized in that: The efficiency coefficient E of the i-th maintenance type i include: Among them, T min is the minimum average maintenance time among all maintenance types, σ i is the standard deviation of the maintenance time for the ith maintenance type, σ max is the maximum standard deviation of maintenance time among all maintenance types, C avg is the average number of maintenance personnel required for all types of maintenance, P i is the cost required for the i-th maintenance type, P max is the maximum cost required for all maintenance types, α′ is the first weighting factor of the efficiency coefficient, β′ is the second weighting factor of the efficiency coefficient, γ′ is the third weighting factor of the efficiency coefficient, and δ′ is the fourth weighting factor of the efficiency coefficient.

9. A balanced maintenance system for a fully automatic vehicle base as claimed in claim 7, characterized in that: The priority coefficient L of the i-th maintenance type i include: Among them, F i is the occurrence frequency of the ith maintenance type, F max is the maximum occurrence frequency among all maintenance types, η′ is the first weight of the priority coefficient, I i is the score of the impact of the i-th maintenance type on train failure, I max is the score for the greatest impact among all maintenance types, θ′ is the second weight of the priority coefficient, U i Score the importance of the i-th maintenance type, U max is the maximum priority score among all maintenance types, and κ′ is the third weight of the priority coefficient.

10. The balancing maintenance system for a fully automatic vehicle base as claimed in claim 7, characterized in that: It also includes setting constraints: Wherein, β″ is the first adjustment factor, and γ′ is the second adjustment factor.

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